In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing-by-design problem appropriately. Consensus in the literature is to handle this problem with complete case analysis, as the missing data are assumed to arise under an appropriate missing at random mechanism that gives consistent estimates here. Specifically, the missing values can reasonably be assumed not to be related to the survival time. In this article, we investigate the potential for multiple imputation to handle this problem in a relevant study on survival after kidney transplantation, and show that it comprehensively outperforms complete case analysis on a range of measures. This is a particularly important finding in the medical context as imputing large amounts of missing data is often viewed with scepticism.

中文翻译：

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将移植存活数据中设计产生的缺失值相乘

在本文中，我们解决了移植存活研究中出现的数据缺失问题。器官移植受者在移植后进行随访，记录他们的存活时间，以及各种解释变量。由于不同中心的数据收集程序或随着时间的推移存在差异，特定的解释变量（或一组变量）可能只记录某些接收者，这导致数据中大量记录缺少该变量。该变量也可能成为生存的重要预测因素，因此适当处理这个设计缺失问题很重要。文献中的共识是通过完整的案例分析来处理这个问题，因为假设缺失数据是在适当的随机缺失机制下产生的，该机制在这里给出了一致的估计。具体来说，可以合理地假设缺失值与生存时间无关。在本文中，我们在一项有关肾移植后存活率的相关研究中调查了多重插补处理该问题的潜力，并表明它在一系列措施上的综合表现优于完整的病例分析。这是医学背景下的一个特别重要的发现，因为通常会以怀疑的态度看待大量缺失数据的估算。我们在一项关于肾移植后存活率的相关研究中调查了多重插补处理这个问题的潜力，并表明它在一系列措施上全面优于完整的病例分析。这是医学背景下的一个特别重要的发现，因为通常会以怀疑的态度看待大量缺失数据的估算。我们在一项关于肾移植后存活率的相关研究中调查了多重插补处理这个问题的潜力，并表明它在一系列措施上全面优于完整的病例分析。这是医学背景下的一个特别重要的发现，因为通常会以怀疑的态度看待大量缺失数据的估算。